CLEF

This paper describes our participation at PAN 2014 author profiling task. Our idea was to define, develop and evaluate a simple machine learning classifier able to guess the gender and the age of a given user based on his/her texts, which could become part of the solution portfolio of the company. We were interested in finding not the best possible classifier that achieves the highest accuracy, but to find the optimum balance between performance and throughput using the most simple strategy and less dependent of external systems. Results show that our software using Naive Bayes Multinomial with a term vector model representation of the text is ranked quite well among the rest of participants in terms of accuracy.

This paper describes the first set of experiments defined by the MIRACLE (Multilingual Information RetrievAl for the CLEf campaign) research group for some of the cross language tasks defined by CLEF. These experiments combine different basic techniques, linguistic-oriented and statistic-oriented, to be applied to the indexing and retrieval processes.

This paper describes the development of a question answering system for the RomanianEnglish cross-lingual track organized within the Cross Lingual Evaluation Forum (CLEF) 2006 campaign. The development stages of our cross-lingual Question Answering (QA) system are described incrementally throughout the paper, at the same time pinpointing the problems that occurred and the way they were addressed. Our system adheres to the classical architecture for QA systems, debuting with question processing followed, after term translation, by information retrieval and answer extraction. Besides the common QA di‐culties, the track posed some speciflc problems, such as the lack of a reliable translation engine from Romanian to English, and the need to evaluate each module individually for a better insight into the system’s failures.

The paper describes QUANTICO, a cross-language open domain question answering system for German and English. The main features of the system are: use of preemptive off-line document annotation with syntactic information like chunk structures, apposition constructions and abbreviation-extension pairs for the passage retrieval; use of online translation services, language models and alignment methods for the cross-language scenarios; use of redundancy as an indicator of good answer candidates; selection of the best answers based on distance metrics defined over graph representations. Based on the question type two different strategies of answer extraction are triggered: for factoid questions answers are extracted from best IR-matched passages and selected by their redundancy and distance to the question keywords; for definition questions answers are considered to be the most redundant normalized linguistic structures with explanatory role (i.e., appositions, abbreviation’s extensions). The results of evaluating the system’s performance by CLEF were as follows: for the best German-German run we achieved an overall accuracy (ACC) of 42.33% and a mean reciprocal rank (MRR) of 0.45; for the best English-German run 32.98% (ACC) and 0.35 (MRR); for the German-English run 17.89% (ACC) and 0.17 (MRR).

This paper describes the work done by Priberam in the development of a question answering (QA) system for Portuguese. The system was built using the company’s natural language processing (NLP) workbench and information retrieval technology. Special focus is given to question analysis, document and sentence retrieval, as well as answer extraction stages. The paper discusses the system’s performance in the context of the QA@CLEF 2005 evaluation.

Abstract. This paper presents QA@L2F, the question-answering sys-tem developed at L2F, INESC-ID. QA@L2F follows different strategies according with the question type, and relies strongly on named entity recognition and on the pre-detection of linguistic patterns. Each ques- ...

The main goal of this paper it is to present our experiments in ImageCLEF 2010 Campaign (Wikipedia retrieval task). This edition we present a different way of using textual and visual information based on the assumption that the textual module better captures the meaning of a topic. So that, the TBIR module works firstly and acts as a filter, and the CBIR system reorder the textual result list. The CBIR system presents three different algorithms: the automatic, the query expansion and a logistic regression relevance feedback algorithm. We have submitted nine textual and eleven mixed runs. Our best run, at the 34 th position (25% at the first result list), is a textual run using our own implemented algorithm based on a VSM approach and TF-IDF weights (included in the IDRA tool) and all languages for annotation and for the topics. Our best mixed run (51th position is at 60% first result list) is using the textual list and the logistic regression relevance algorithm at the CBIR module....

We tried Named Entity features of source documents to identify its suspicious counter part. A three stage identification method was adopted to understand the impact of NEs in plagiarism. Results along with a brief analysis are given in this note.

This paper describes our participation in the CLEF bilingual retrieval task (formulating queries in Spanish to retrieve documents in English), using an information retrieval (IR) system based on the vector model. Our aim was to use a simple approach to solve the problem, without expecting to obtain great results, especially owing to the short time available. The queries formulated in Spanish were translated to English by a commercial machine translation system. The translations were filtered to eliminate stop words, and then the remaining terms were stemmed using a standard stemmer. Results were poorer than those obtained through monolingual retrieval with original English queries, the difference being slightly over 15%.